Compare Popular Python Machine Learning Libraries

Python is one of the most popular programming languages ​​worldwide, with a growing number of libraries and frameworks to facilitate AI and ML development. With over 250 libraries in Python, it can be a bit confusing to know which one is best for your project and keep up with the technological changes and trends that all come with them.

Below are the popular Python machine-learning libraries I used. I do my best to sort them out which one to use for which scenario. There are a ton more libraries out there, but I can’t speak to libraries that I haven’t used, and I think these are the most used ones.


Unlike other machine learning packages, NumPy is a well-known general-purpose array-processing package. For nth-dimensional arrays (vectors, matrices, and higher order matrices), NumPy provides high-performance (natively compiled) support for and support for a variety of operations. It enables vectorized operations, in particular, which translate Python expressions into low-level code dispatch that implicitly loops over different subsets of data.

NumPy Functions

  • numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)

The function’s start and stop arguments, both of which are required, return values ​​evenly spaced over a predetermined interval.

The elements of the array are repeated with the numpy.repeat(a, repeats, axis = None) method. The second input repeat specifies the number of repetitions.

Returns a random integer from the function numpy.random.randint(low, high=none, size=none, dtype=”l”) [low, high], Random numbers are chosen from the range [0, low] If the high parameter is absent (none).

Why is Numpy so popular?

Simply put, NumPy optimized and pre-compiled C code that handles all the heavy lifting, making it faster than standard Python arrays.

Many mathematical procedures frequently used in scientific computing are made quick and simple to use by NumPy.


pandas is fast becoming the most widely used Python library for data analysis as it supports quick, adaptable, and expressive data structures that deal with both “relational” and “labeled” data. There are practical and real-world Python data analysis problems that require Pandas. Pandas provide fully optimized and highly reliable performance. Only C or Python is used to write the backend code purely.

some pandas tasks

The first function to mention is read_csv or read_excel. The works already provide a clear explanation. I used them to read data from csv or excel files into pandas dataframe format.

df = pd.read_csv("PlayerStat.csv") csv() function can also read txt files using the following syntax:

data = pd.read_csv(file.txt, sep=" ")

A Boolean expression can filter or query data. I can apply filtering criteria using query function as a string. Compared to many other processes, it offers more freedom.

df.query("A > 4")

Only rows where A is greater than four will be returned.

I passed the rows and column indices as parameters to this function, which returns the appropriate subset of the dataframe.

Another highly original and popular work. One must know the data types of the variables before starting any analysis, visualization, or predictive modelling. Using this technique, you can get the data type for each column.


panda vs wax

Vaex Python is an alternative to the pandas library that uses out-of-core DataFrames to compute large amounts of data more quickly. Vaex is a high-performance Python module for lazy out-of-core dataframes (compared to Pandas) for viewing and studying large tabular datasets. More than a billion rows per second can be calculated using simple statistics. This enables a variety of visualizations that will allow for considerable data exploration that is interactive.


TensorFlow is a Python library for accelerated numerical computing created and released by Google. Tensorflow uses language and function names that are somewhat different from Theano, which can make switching from Theano more complicated than it needs to be. However, the entire computing graph in Tensorflow operates similarly to Theano, with similar advantages and disadvantages. Even though modifications to the computation graph have a significant impact on performance, Tensorflow’s eval function just makes it a little easier to observe the intermediate state. Tensorflow is the preferred deep learning technology compared to Theano and Caffe of a few years back.

TensorFlow built-in functions

The output of this function is a tensor with the same type and shape as the input tensor but with a value of zero.

tensor = tf.constant( I[1, 2, 3], [4, 5, 6]])

tf.zeros_like( tensor) # [ [0, 0, 0], [0, 0,0]

When creating a black image from an input image, this function can be helpful. If you want to define the form directly then use tf.zeros. If you prefer to initialize to one instead of zero, use tf.ones_like.

Adds the specified padding around it with a constant value to increase the dimension of a tensor.

It helps you when you run TensorFlow applications. When using eager execution, you don’t need to build and run the graph in a single session. Here’s more information about eager execution.

“eager execution” should be the first statement after importing TensorFlow.

TensorFlow vs PyTorch

Pytorch, a Python implementation of Torch, is supported by Facebook. It competes with the above techniques by providing just-in-time graph compilation, which makes Pytorch code more compatible with the surrounding Python by not treating graphs as distinct and opaque objects. Instead, there are a number of flexible techniques to piece together tensor computations on the fly. Besides that, it performs well. It has strong multi-GPU capability, much like Tensorflow; However, Tensorflow still prevails for more substantial distributed systems. While Pytorch’s API is well documented, Tensorflow or Keras are more polished. However, Pytorch wins in flexibility and usability without compromising performance, and this undoubtedly forces Tensorflow to rethink and adjust. Tensorflow has recently been severely challenged by Pytorch, prompting the Google team to optimize.


Keras is an open-source software library that provides a Python interface for artificial neural networks. Since Keras is nominally independent of the engine, the Keras code can theoretically be reused even if the engine needs to be changed for performance or other factors. The drawback of this is that you usually need to employ Tensorflow or Theano under the Keras layer whenever you want to build very novel or specialized architectures. This mainly happens when you need to employ sophisticated NumPy sequencing, which matches the assemble/scatter in Tensorflow and set/inc subtensors in Theano.

keras function

evaluate() and predict() are both available in Keras. These techniques can use NumPy datasets. When the data was tested I finished evaluating the result. I used these techniques to assess my models.

Each Keras layer consists of several techniques. These layers help in creating, configuring and training the data. The dense layer helps in operation implementation. I flattened the input using flat. Dropout Input Enables dropout. I can reshape the output using reshape tool. I started a keras tensor using input.

A fairly simple library is Keras. This makes it possible to obtain output from the intermediate of a layer. You can easily add a new layer to the existing layer to help you get the output in intermediate.


Theano is a Python library and compiler optimized for manipulating and evaluating mathematical expressions, especially matrix-valued ones. Being the oldest and most established offers advantages and disadvantages for Theano. Most of the user-requested features have been added as this is an older version. However, some of these implementations are a bit too complex and challenging to use because there were no prior examples to follow. The documentation is still unclear. Since there is no easy way to check intermediate calculations, it can be very challenging to get a complex project to function properly in Theano. They are usually debugged using debuggers or by looking at the computation graph.

theano function

I declare a decimal scalar variable with the dscalar method. When the statement below is run, it adds a variable named C to your program’s code.

C = tensor.dscalar()

The function accepts two arguments, the first of which is the input and the second is the output of the function. As per the declaration below, the first parameter is an array with two items c and d. The result is a scalar unit designated as E.

f = theano.function([C,D], E)


I’ve seen a highly skilled Python programmer quickly pick up on the subtleties of a new library and understand how to use it. However, whether as a beginner, intermediate, or expert, choosing one programming language or, on occasion, one library over another depends on the goals and needs of your project.

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